For each method, we use a batch size of 32 and grid search for a learning rate (options: \(\lbrace 1e^{-2}, 1e^{-3}, 1e^{-4}\rbrace \) ) and weight decay for L2 regularization (options: \(\lbrace 1e^{-3}, 1e^{-4}, 1e^{-5}\rbrace \) ) using our validation data.
The validation data is a random 10% of the training dataset and we repeat this random splitting five times.
In our experiments, since we use a GRU-D [1]} to compute prefix embeddings, its hidden state should be updated in between hop sizes. For simplicity, we only update the embeddings when real data are observed per the baselines, though Stop&Hop's final Stop time may be between observations.
Each model was optimized using Adam [2]} with learning rates and weight decays that maximize their performance on the validation data.
All training was done using Intel Xeon Gold 6148 CPUs.
For all experiments, we set the value \(\alpha \)  in Equation REF  to 1 as the loss terms are balanced.
All of our code and data are publicly available.https://github.com/thartvigsen/StopAndHop
